Development of a High-Temporal-Resolution Smart Rock System Integrating Multi-Modal Observations: Trajectory Reconstruction and Impact Inversion

Effective rockfall protection design requires accurate estimation of impact forces and movement trajectories. Current practices predominantly depend on numerical simulations or Optical techniques to inversely derive kinematic parameters. However, these optical methods are limited by occlusion, perspective distortion, and the inability to capture high-frequency internal impact dynamics. While previous studies have utilized MEMS-embedded Smart Rocks to monitor internal states and reconstruct 4D trajectories, existing devices are often constrained by hardware specifications. Insufficient sampling rates (typically below 1 kHz) fail to capture millisecond-level impact peaks, resulting in signal aliasing, while sensor saturation during high-intensity collisions leads to attitude divergence during attitude estimation.

To address these limitations, this study presents "Smart Rock Node (SaRoN)", a smart sensing module embedded in a 30 cm reinforced concrete shell. This design ensures the probe's mechanical properties, specifically density and coefficient of restitution, closely mimic natural boulders, ensuring the kinetic data reflects realistic rockfall behavior. It features a 1600 Hz sampling rate to prevent peak clipping and integrates a dual-sensor architecture, combining a high-G accelerometer (±200 g) and a precision IMU (±16 g, ±4000 dps), to ensure a wide dynamic range. The hardware employs a centrally-mounted computing unit with a ring buffer to eliminate data writing latency. On the algorithmic level, we introduce an adaptive impact-gating mechanism. This algorithm dynamically decouples the gravity vector dependence during collision moments, automatically pausing acceleration correction to mitigate filter divergence. This is complemented by a 1 ms timestamp synchronization protocol, ensuring precise temporal alignment for robust multi-sensor fusion. Reliability and accuracy were validated through pendulum, free-fall, and shaking table experiments, confirming trajectory consistency, structural robustness, and acceleration fidelity. Notably, Power Spectral Density (PSD) and Magnitude Squared Coherence (MSC) analyses were employed to calibrate the frequency response and confirm the credibility of event frequencies across operational bands. For field validation, a full-scale experiment is planned for the Jinheng Park slope in Taroko Gorge. The setup integrates SaRoN with a multi-modal observation network: SmartSolo and geophones to pinpoint impact locations via seismic signals, while Distributed Acoustic Sensing (DAS) installed on rockfall sheds monitors structural stress waves to assess impact intensity, UAV combined with ArUco markers serves as ground truth for validating attitude and trajectory verification.

Results demonstrate that the SaRoN system mitigates signal saturation during high-intensity impacts and shows good agreement with ground truths, highlighting its potential for capturing complex rockfall dynamics, providing high-fidelity kinematic data essential for advancing rockfall protection engineering.

Keywords: Trajectory Reconstruction, Smart Rock Node (SaRoN), Rockfall Impact force, Distributed Acoustic Sensing (DAS).